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2016•Unpublished venueRequires access

State of Charge, State of Health and State of Function Co-Estimation of Lithium-Ion Batteries for Electric Vehicles

Ping Shen, Minggao Ouyang, Languang Lu, Jianqiu Li

Open publisher page 17 citations

Abstract

This paper proposed a co-estimation scheme of State of Charge (SOC), State of Health (SOH), and State of Function (SOF) for lithium-ion batteries. The Extended Kalman Filter is adopted to SOC estimation. Battery parameters are identified online by using Recursive Least Square Algorithm to further estimate battery SOH and SOF. The accuracy of the estimation is improved and the computation is reduced by making good use of the correlations among the states.

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What this paper is about

This paper proposed a co-estimation scheme of State of Charge (SOC), State of Health (SOH), and State of Function (SOF) for lithium-ion batteries. The Extended Kalman Filter is adopted to SOC estimation. Battery parameters are identified online by using Recursive Least Square Algorithm to further estimate battery SOH and SOF. The accuracy of the estimation is improved and the computation is reduced by making good use of the correlations among the states.

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OpenAlex reports 17 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper proposed a co-estimation scheme of State of Charge (SOC), State of Health (SOH), and State of Function (SOF) for lithium-ion batteries. The Extended Kalman Filter is adopted to SOC estimation. Battery parameters are identified online by using Recursive Least Square Algorithm to further estimate battery SOH and SOF. The accuracy of the estimation is improved and the computation is reduced by making good use of the correlations among the states.

Key concepts: State of charge, State of health, Kalman filter, Battery (electricity), State (computer science), Computation, Estimation, Extended Kalman filter

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